A comparative analysis of optical character recognition models for extracting and classifying texts in natural scenes

Abstrak

This research introduces prior-guided dynamic tunable network (PDTNet), an efficient model designed to improve the detection and recognition of text in complex environments. PDTNet’s architecture combines advanced preprocessing techniques and deep learning methods to enhance accuracy and reliability. The study comprehensively evaluates various optical character recognition (OCR) models, demonstrating PDTNet’s superior performance in terms of adaptability, accuracy, and reliability across different environmental conditions. The results emphasize the need for a context-aware approach in selecting OCR models for specific applications. This research advocates for the development of hybrid OCR systems that leverages multiple models, aiming to arrive at a higher accuracy and adaptability in practical applications. With a precision of 85%, the proposed model showed an improved performance of 1.7% over existing state of the arts model. These findings contribute valuable insights into addressing the technical challenges of text extraction and optimizing OCR model selection for real-world scenarios.